Challenges in Modeling, Interpreting, and Drawing Conclusions from Images as Forensic Evidence
Date
2024-10-11
Authors
Kafadar, Karen
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Taylor & Francis Group, LLC
Abstract
When a crime is committed, law enforcement directs crime scene experts to obtain evidence that may be pertinent to identifying the perpetrator(s). Much of this evidence comes in the form of images, either digitally transcribed (e.g.,: fingerprints, handwriting), or as digital photographs (e.g., biometric images, photographs of patterns created by blood spatter or arson). Finding models that faithfully capture the “key features” in these images is critical: attribution of the evidence will be accurate only if these “key features” can be properly compared across different images. The huge variety in the types, shapes, and locations of such features leads to challenges in obtaining valid inferences. We describe some of these challenges, discuss some prior approaches, and suggest future directions which need to be pursued to avoid miscarriages of justice that have occurred in the absence of statistically-validated methods of inference for forensic evidence.
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This article is published as Kafadar, K., & Carriquiry, A. L. (2024). Challenges in Modeling, Interpreting, and Drawing Conclusions from Images as Forensic Evidence. Statistics and Data Science in Imaging, 1(1). https://doi.org/10.1080/29979676.2024.2401758.
Rights Statement
© 2024 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.